ABSTRACT
HTTP adaptive streaming (HAS) is being adopted with increasing frequency and becoming the de-facto standard for video streaming. However, the client-driven, on-off adaptation behavior of HAS results in uneven bandwidth competition and this is exacerbated when a large number of clients share the same bottleneck network link and compete for the available bandwidth. With HAS each client independently strives to maximize its individual share of the available bandwidth, which leads to bandwidth competition and a decrease in end-user quality of experience (QoE). The competition causes scalability issues, which are quality instability, unfair bandwidth sharing and network resource underutilization. We propose a new software defined networking (SDN) based dynamic resource allocation and management architecture for HAS systems, which aims to alleviate these scalability issues and improve the per-client QoE. Our architecture manages and allocates the network resources dynamically for each client based on its expected QoE. Experimental results show that the proposed architecture significantly enhances scalability by improving per-client QoE by at least 30% and supporting up to 80% more clients with the same QoE compared to the conventional schemes.
References
- S. Akhshabi, L. Anantakrishnan, A. C. Begen, and C. Dovrolis. What Happens when HTTP Adaptive Streaming Players Compete for Bandwidth? In Proceedings of the 22nd international workshop on Network and Operating System Support for Digital Audio and Video, pages 9--14, 2012. Google Scholar
Digital Library
- S. Akhshabi, S. Narayanaswamy, A. C. Begen, and C. Dovrolis. An Experimental Evaluation of Rate-daptive Video Players over HTTP. Signal Processing: Image Communication, 27(4):271--287, 2012. Google Scholar
Digital Library
- E. F. Camacho and C. B. Alba. Model Predictive Control. Springer Science & Business Media, 2013.Google Scholar
Digital Library
- DASH External Dataset. http://www-itec.uni-klu.ac.at/ftp/datasets/DASHDataset2014/.Google Scholar
- DASH Industry Forum. http://www.dashif.org/.Google Scholar
- P. Georgopoulos, Y. Elkhatib, M. Broadbent, M. Mu, and N. Race. Towards Network-wide QoE Fairness using Openflow-assisted Adaptive Video Streaming. In Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking, pages 15--20, 2013. Google Scholar
Digital Library
- T.-Y. Huang, R. Johari, N. McKeown, M. Trunnell, and M. Watson. Using The Buffer to Avoid Rebuffers: Evidence From a large Video Streaming Service. arXiv preprint arXiv:1401.2209, 2014.Google Scholar
- C. V. N. Index. The Zettabyte Era--Trends and Analysis. Cisco white paper, 2014.Google Scholar
- J. Jiang, V. Sekar, and H. Zhang. Improving Fairness, Efficiency, and Stability in HTTP-based Adaptive Video Streaming with Festive. In Proceedings of the ACM 8th international conference on Emerging networking experiments and technologies, pages 97--108, 2012. Google Scholar
Digital Library
- P. Juluri, V. Tamarapalli, and D. Medhi. SARA: Segment Aware Rate Adaptation Algorithm for Dynamic Adaptive Streaming over HTTP. In Communication Workshop (ICCW), 2015 IEEE International Conference on, pages 1765--1770, 2015.Google Scholar
Cross Ref
- V. Krishnamoorthi, N. Carlsson, D. Eager, A. Mahanti, and N. Shahmehri. Helping Hand or Hidden Hurdle: Proxy-assisted Http-based Adaptive Streaming Performance. In Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2013 IEEE 21st International Symposium on, pages 182--191, 2013. Google Scholar
Digital Library
- Z. Li, A. C. Begen, J. Gahm, Y. Shan, B. Osler, and D. Oran. Streaming Video over HTTP With Consistent Quality. In Proceedings of the 5th ACM Multimedia Systems Conference, pages 248--258, 2014. Google Scholar
Digital Library
- Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. Begen, and D. Oran. Probe and Adapt: Rate Adaptation for HTTP Video Streaming at Scale. Selected Areas in Communications, IEEE Journal on, 32(4):719--733, 2014.Google Scholar
- F. Markus, H. Tobias, and P. Tran-Gia. A Generic Quantitative Relationship between Quality of Experience and Quality of Service. IEEE Network Magazine, 2010.Google Scholar
- N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, and J. Turner. OpenFlow: Enabling Innovation in Campus Networks. ACM SIGCOMM Computer Communication Review, 38(2):69--74, 2008. Google Scholar
Digital Library
- Mininet. http://mininet.org/.Google Scholar
- R. K. P. Mok, X. Luo, E. W. W. Chan, and R. K. C. Chang. QDASH: A QoE-aware DASH System. In Proceedings of the 3rd Multimedia Systems Conference, MMSys '12, pages 11--22, New York, NY, USA, 2012. Google Scholar
Digital Library
- A. Rehman, K. Zeng, and Z. Wang. Display Device-adapted Video Quality-of-experience Assessment. In IS&T/SPIE Electronic Imaging, pages 939406--939406. International Society for Optics and Photonics, 2015.Google Scholar
- RYU SDN Framework. http://osrg.github.io/ryu/.Google Scholar
- M. Seufert, S. Egger, M. Slanina, T. Zinner, T. Hobfeld, and P. Tran-Gia. A Survey on Quality of Experience of HTTP Adaptive Streaming. Communications Surveys & Tutorials, IEEE, 17(1):469--492, 2015.Google Scholar
Digital Library
- Software Defined Networking (SDN). https://www.opennetworking.org/.Google Scholar
- T. Stockhammer. Dynamic Adaptive Streaming over HTTP--: Standards and Design Principles. In Proceedings of the second annual ACM conference on Multimedia systems, pages 133--144, 2011. Google Scholar
Digital Library
- E. Thomas, M. van Deventer, T. Stockhammer, A. C. Begen, and J. Famaey. Enhancing MPEG DASH Performance via Server and Network Assistance. Stevenhage: IET, 2015.Google Scholar
Cross Ref
Index Terms
SDNDASH: Improving QoE of HTTP Adaptive Streaming Using Software Defined Networking





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